Classified Characterization Method for Connectivity of Organic Matter (om)-Hosted Pores in Shale

1. A classified characterization method for connectivity of organic matter (OM)-hosted pores in shale, comprising the following steps: S 100 : scanning a shale sample according to a preset imaging area through a scanning electron microscope to acquire a two-dimensional (2D) image of the shale sample; S 200 : extracting pore parameters of each OM in the 2D image by Avizo software, wherein the pore parameters comprise porosity φ OM , median pore radius Md, pore sorting coefficient S o , and pore shape factor SF of the OM; S 300 : acquiring a class number of OM sets according to the pore parameters, wherein step S 300 comprises: S 310 : performing an initial classification on the OM sets through a K-means clustering algorithm based on the porosity, the median pore radius, the pore sorting coefficient and the pore shape factor, wherein an initial class number of the OM sets is K, K≥2, and each of the OM sets comprises N OM, wherein N is a natural number greater than 1; S 320 : calculating a coefficient of variation CV j of each class of OM sets through the K-means clustering algorithm, CV j =σ j /μ j , wherein j is φ OM , Md, S o or SF; σ j is a standard deviation (SD) of corresponding j of the N OM in each class of OM sets; and μ j is a mean of the corresponding j of the N OM in each class of OM sets; and S 330 : when any coefficient of variation CV j in the K classes of OM sets satisfies CV j ≤0.5, taking the initial class number K of the OM sets as the class number of the OM sets; and otherwise, proceeding to S 340 ; and S 340 : setting the initial class number of the OM sets to K=K+1, and repeating S 320 to S 330 ; S 400 : performing three-dimensional (3D) reconstruction on each class of OM sets through a focused ion beam-helium ion microscope to acquire reconstructed 3D models of the OM; S 500 : acquiring a pore connectivity parameter based on the reconstructed 3D models of the OM by the Avizo software, wherein the pore connectivity parameter is P m , P m =V Cm /V Tm ; P m is a pore connectivity parameter of an m-th class of OM set, V Cm is a volume of connected pores in the m-th class of OM set, and V Tm is a volume of total pores in the m-th class of OM set; mϵ[1, K], wherein K is the class number of the OM sets; and S 600 : acquiring an evaluation index for overall connectivity of the OM-hosted pores in the shale based on the pore connectivity parameter.

2. The classified characterization method according to claim 1 , wherein a method for acquiring the 2D image comprises: S 110 : performing argon ion polishing on the shale sample to acquire a pretreated shale sample; S 120 : setting an acceleration voltage of the scanning electron microscope to 1.2 kV, and acquiring a secondary electron signal on a surface of the pretreated shale sample at a preset imaging resolution; and S 130 : performing, by the scanning electron microscope, continuous 2D splicing imaging on the pretreated shale sample according to the preset imaging area, wherein the preset imaging area is S, and S>100 μm*100 μm.

3. The classified characterization method according to claim 2 , wherein the preset imaging resolution is 4 nm/pixel; and the preset imaging area is 260 μm*260 μm.

4. The classified characterization method according to claim 2 , wherein a method for extracting the pore parameters comprises: S 210 : preprocessing the 2D image to acquire a preprocessed image, wherein the preprocessing comprises grayscale correction and filtering; S 220 : extracting all OM in the preprocessed image by threshold segmentation based on a grayscale difference, and storing the extracted all OM in a binarized form to acquire a binarized image; S 230 : acquiring a first OM region image based on the binarized image, wherein the first OM region image comprises N first OM regions, and each of the N first OM regions has a closed-loop structure composed of a same grayscale; S 240 : assimilating different grayscales in the N first OM regions through a filling command to acquire a filled second OM region image, wherein the filled second OM region image comprises N second OM regions, and the N second OM regions correspond to the N first OM regions, respectively; S 250 : sequentially assigning values to the N second OM regions through MATLAB software to acquire a third OM region image with different assigned regions, wherein the third OM region image comprises N third OM regions, and assignment numbers of the N third OM regions are defined by a first set of Arabic numerals in a first order; S 260 : sequentially assigning values to the N first OM regions through the MATLAB software to acquire a fourth OM region image with different assigned regions, wherein the fourth OM region image comprises N fourth OM regions, and assignment numbers of the N fourth OM regions are defined by a second set of Arabic numerals in a second order; and the second order is set in accordance with the first order, and the first set of Arabic numerals is set in accordance with the second set of Arabic numerals; S 270 : subtracting the third OM region image from the fourth OM region image to acquire a fifth OM region image, wherein the fifth OM region image comprises N fifth OM regions, and the N fifth OM regions are labeled pore images, wherein values are assigned to the labeled pore images sequentially; and S 280 : acquiring the porosity φ OM , the median pore radius Md, the pore sorting coefficient S o and the pore shape factor SF based on the fifth OM region image; wherein, φ OM =A p /(A QM +A p )*100%, A OM is an area of a single OM, and A P is an area of pores in the single OM; the median pore radius Md is a pore radius corresponding to 50% on a cumulative pore size distribution curve, wherein the cumulative pore size distribution curve is a distribution curve of a pore radius and a cumulative percentage of a pore area; and S o =P 25 /P 75 , wherein, P 25 and P 75 respectively represent pore radii corresponding to cumulative percentages 25% and 75% of the pore area on the cumulative pore size distribution curve; calculating N pore shape factors SF i corresponding to the N fifth OM regions, SF i =4πA pi /L pi 2 , wherein SF i is a pore shape factor of an i-th pore, A pi is an area of the i-th pore, and L pi is a perimeter of the i-th pore, iϵ[1, N]; selecting pore shape factors with a same value to form a set of identical factors, wherein the N pore shape factors form M sets of identical factors, and Mϵ(1, N); wherein, each of the sets of identical factors comprises n pore shape factors, nϵ(1, N); acquiring a sum of areas of pores corresponding to each set of identical factors to form a set of pore areas, wherein the M sets of identical factors form M sets of pore areas; and acquiring, based on the M sets of pore areas, a pore shape factor corresponding to a set of pore areas with a maximum sum of areas to be the pore shape factor SF.

5. The classified characterization method according to claim 4 , wherein σ φ O ⁢ M = ∑ i = 1 N ⁢ ( φ OM , i - μ φ OM ) 2 N , wherein σ φOM is standard deviation, (SD) of porosities corresponding to the N OM in each class of OM sets; μ φ O ⁢ M = ∑ i = 1 N ⁢ φ OM , i N , wherein μ φOM is a mean of porosities corresponding to the N OM in each class of OM sets; σ M ⁢ d = ∑ i = 1 N ⁢ ( M ⁢ d i - μ M ⁢ d ) 2 N , wherein σ Md is an SD of median pore radii corresponding to the N OM in each class of OM sets; μ M ⁢ d = ∑ i = 1 N ⁢ M ⁢ d i N , wherein μ Md is a mean of median pore radii corresponding to the N OM in each class of OM sets; σ S o = ∑ i = 1 N ⁢ ( S o , i - μ S o ) 2 N , wherein σ S o is an SD of pore sorting coefficients corresponding to the N OM in each class of OM sets; μ S o = ∑ i = 1 N ⁢ S o , i N , wherein μ S o is a mean of pore sorting coefficients corresponding to the N OM in each class of OM sets; σ S ⁢ F = ∑ i = 1 N ⁢ ( S ⁢ F i - μ S ⁢ F ) 2 N , wherein σ SF is an SD of pore shape factors corresponding to the N OM in each class of OM sets; and μ S ⁢ F = ∑ i = 1 N ⁢ SF i N , wherein μ SF is a mean of pore shape factors corresponding to the N OM in each class of OM sets.

6. The classified characterization method according to claim 5 , wherein a method for acquiring the reconstructed 3D models of the OM comprises: screening the K classes of OM sets based on a preset area threshold by the focused ion beam-helium ion microscope to acquire the reconstructed 3D models of the OM, wherein the preset area threshold is S1, S1>5 μm*5 μm.

8. The classified characterization method according to claim 4 , wherein SFϵ(0,1].